In this context, we want to query each node by taking into account both 1) the residual degree (as high as possible) and 2) the sum of the shortest paths to potentially influential nodes (as low as possible). To this end, starting from the reinforced weighted graph \(G^{(t)}_\text{gen-prun}\) , we first execute an IM algorithm to find the set of \(k\)  potentially influential nodes at the \(t\) -th query step, denoted as \(\hat{S}^{(t)} \subseteq \mathcal {V}\) .In our work, degree discount heuristics [1]} are adopted to find \(\hat{S}^{(t)}\)  for computational efficiency. Then, if a node \(v \in \hat{S}^{(t)}\)  belongs to the current explored subgraph (i.e., \(v \in \mathcal {V}_t\) ), it is excluded from \(\hat{S}^{(t)}\)  (i.e., \(\hat{S}^{(t)} \leftarrow \hat{S}^{(t)}\setminus \lbrace v\rbrace  \) ) so that only the potentially influential nodes in the unexplored part are possible candidates to be queried. Using the set \(\hat{S}^{(t)}\) , we now propose our own topology-aware ranking strategy by defining a ranking measure:
\(\text{rank}^{(t)}(u) = r_u^{(t)} -\alpha \sum _{v \in \hat{S}^{(t)}}\text{GD}^{(t)}(u,v),\) 
